Did you use the internet today? Visit Facebook? Buy from Amazon? Check the weather on your smartphone. Watch a video on YouTube. Use Uber? Join LinkedIn? Write to your colleague. You have produced data if you have engaged in any of these activities. And among the billions who provide data, you are but one. In reality, 2.5 quintillion bytes of data being produced everyday by Internet users, and 90% of the data we have now was created in the past two years alone.
For firms and organizations who can take this information to heart and use it to their advantage, explosive growth seems like a reality. This data is pointless without ways to capture and analyze it. However, a fact is driving strong demand for data science professionals.
What Does A Data Scientist Do?
Data scientists are professionals adept at extracting meaningful information from data and interpreting it, which includes statistical and machine learning tools, techniques, and human understanding. Since the data is never pure, it takes a lot of effort to collect, clean, and search the data.
Data scientists examine the questions that need to be addressed and the locations of the pertinent data. In addition to having business understanding and analytical skills, they are also skilled in data mining, cleaning, and presentation. Unstructured data is acquired, managed, and analyzed by businesses using data scientists.
In summary, a data scientist may perform the following tasks daily:
Find patterns and trends in datasets and uncover insights
Build algorithms and data models to predict outcomes
Utilize machine learning techniques to enhance product offers or data quality
Recommendations should be communicated to other teams and executives.
Utilize data analytic tools like Python, R, SAS, or SQL.
Keep abreast of data science advancements
Many chances and good pay are promised with a profession in data science. Data science was ranked the sexiest profession in the 21st century by Harvard Business Review. With a rating of 4.8 out of 5 and a satisfaction rating of 4.2 out of 5, Glassdoor ranks data scientist as the top job in the US. The median base income is $110,000, and there are thousands of open positions right now in addition to many more.
Although the job potential sounds great, you might be curious as to what a data scientist actually works all day. We’ve compiled data to assist you comprehend the day-to-day responsibilities of data scientists so you can see yourself in the position and choose whether it’s time to start training for it.
A Day In The Life Of A Data Scientist Working With The Ubiquitous Data
Data is crucial to a data scientist’s everyday tasks, as would be expected given the nature of the position. Most of the time, data scientists acquire, view, and shape data, but they do it in a variety of ways and for a range of objectives. Among the tasks a data scientist might take on are the following:
Downloading Data
Data Merging
Data Analysis
Looking for patterns or trends
Using a wide range of tools, including R, Tableau, Python, Matlab, Hive, Impala, PySpark, Excel, Hadoop, SQL, and/or SAS
Development and testing of new algorithms
An attempt to simplify data problems.
Development of predictive models
Visualization of building data
Logging results to share with others.
Collecting proofs of concepts
The primary responsibility of data scientists is not related to any of these duties. However, the main focus of data scientists is problem solving. Understanding the aim is necessary for working with this data. Data scientists must also identify the questions that need answers and design different approaches to solve the problem.
Even meetings will revolve around data as you try to understand the problems, which brings us to another part of the non-typical data scientist’s day: communicating with others who are not data experts. It might seem like it plays a minor role in the age of data scientists, but the opposite is true because, ultimately, your job is to solve problems, not build models.
It is important to remember that although a data scientist works with data and numbers, the reason is driven by business needs. The ability to see the big picture from a departmental perspective is critical. So is the ability to understand the market strategy and help people understand the consequences of their decisions.
A data scientist spends time in meetings and answering emails, just like most people in the corporate world. But the ability to communicate may be even more critical for a data scientist. During these meetings and emails, you need to be able to explain the science behind the data so that the layperson can understand and understand their problems as they see them, not as a data scientist sees them.
Keep Up With The Changes
If you choose a career as a data scientist, working with data and more will make up a significant part of your day. The rest of the day will be spent keeping up with the field of data science. New information comes daily as other data scientists figure out how to solve the problem and share their unique insights. Therefore, a data scientist spends most of the day reading industry-related blogs, newsletters, and discussion forums. They can attend conferences or network online with other data scientists. And from time to time, they may be the ones to convey new information.
You don’t want to waste time reinventing the wheel as a data scientist. Want to know if anyone else has a better way to solve the problem. Keeping up with the modifications is the only way you will be able to.
Data Analyst
Data analysts act as a link between business analysts and data scientists. They organize and evaluate the data after receiving inquiries from the organization to produce findings that are in line with the high-level business plan. Data analysts are in charge of converting technical analysis into qualitative action items and effectively presenting their findings to various stakeholders.
Skills required include data manipulation, data visualization, statistical and mathematical knowledge, and programming knowledge (SAS, R, and Python).
What Differentiates A Data Scientist From A Data Analyst?
The roles of data analysts and data scientists may appear to be similar because both search for trends or patterns in data and find fresh approaches to help firms make better operational decisions. However, data scientists are typically given more authority and are regarded as being more senior than data analysts.
Data analystscan assist teams that already have goals in place, whereas data scientists are frequently expected to develop their own questions regarding the data. A data scientist may also spend more time developing models, using machine learning, or incorporating advanced programming to search and analyze data. Many data scientists may begin their careers as data analysts or statisticians.
How To Become A Data Scientist ?
In most cases, proper education is essential to become a data scientist. Here are some ideas for next actions.
Obtain A Data Science Degree
Although it’s not always necessary, employers typically prefer to see proof of your academic accomplishments to confirm that you have the skills to handle a data science position. That said, a related bachelor’s degree can definitely help – try studying data science, statistics, or computer science to get a head start in this field.
Improve The Relevant Skills
If you feel you can brush up on some of your hard data skills, consider taking an online course or enrolling in a relevant Bootcamp. Here are a few skills you’ll want to have under your belt.
Programming Languages:
Data scientists can expect to spend time using programming languages ??to sort, analyze, and manage large chunks of data. Popular programming languages ??for data science include:
Python
R
SQL
SAS
Data Visualization:
The ability to create charts and graphs is essential to a data scientist’s job. Knowledge of the following tools should prepare you for the career:
Live picture
PowerBI
Excel
Machine Learning:
Incorporating machine learning and deep learning into your work as a data scientist means constantly improving the quality of the data you collect and potentially being able to predict the results of future datasets. A machine learning course can help you get started with the basics.
Big Data:
Some employers may want to see that you are familiar with big data. Some software frameworks used to process big data include Hadoop and Apache Spark.
Communication:
The most brilliant data scientists will not be able to affect any change if they cannot communicate their findings well. The ability to share ideas and results verbally and in written language is an often sought-after skill for data scientists.
Get A Primary Data Analysis Job
Although there are many paths to becoming a data scientist, starting a related entry-level job can be a significant first step. Look for positions that work intensively with data, such as data analyst, business intelligence analyst, statistician, or data engineer. From there, you can work towards becoming a scientist as you expand your knowledge and skills.
Prepare For Data Science Interviews
You may feel ready to move into data science with several years of experience working with data analytics. Once you get an interview, prepare answers to likely interview questions.
Data scientist positions can be highly technical, so you may encounter technical and behavioral questions. Anticipate both and practice your answer out loud. Preparing examples from your past work or academic experience can help you appear confident and knowledgeable to interviewers.
Conclusion
Becoming a data scientist may require some training, but you can end up with a demanding and challenging career.
